# Set the working directory
#setwd("~/Desktop")
library(dplyr)
library(ggplot2)
library(knitr)
# Import data into df
#setwd("~/Desktop")
#getwd()
df <- read.csv("11Tame Impala.csv")My SpotifyR report
Tame Impala
WHO is Tame Impala?
Tame Impala is the psychadelic music project of Australian multi-instrumentalist Kevin Parker. In the recording studio, Parker writes, records, performs, and produces all of the project’s music.
As a touring act, Tame Impala consists of Parker (vocals, guitar, synthesizer), Dominic Simper (guitar, synthesiser), Jay Watson (synthesiser, vocals, guitar), Cam Avery (bass guitar, vocals, synthesizer), and Julien Barbagallo (drums, vocals).
Discography
InnerSpeaker (2010)
Lonerism (2012)
Currents (2015)
The Slow Rush (2020)
My favorite album
My favorite album is “Current”. I really like the cover as well.
My favorite song
My favorite song is called “Let It Happen” and it´s also from my favorite album! Let´s listen to it (it´s quite long- so maybe get some snacks)!
My favorite lyrics
She said, “It’s not now or never
Wait ten years, we’ll be together”
I said, “Better late than never
Just don’t make me wait forever”
- The Less I Know the Better, Album: Currents
Data analysis
Data source
I extracted the data using the spotifyr package, and stored it in the data folder.
Import data
Take a glance of the data frame
# Slice 5 random records in the dataframe
sampled_df <- dplyr::slice_sample(df, n = 5)|> knitr::kable(digits = 2)
# Format the sampled data frame as a table with kableExtra
#library(kableExtra)
#table_result <- kable(sampled_df, format = "html", digits = 2) %>% kable_styling()
# Print the formatted table
#cat(table_result)
# Slice 5 random records in the dataframe
df |> dplyr::slice_sample(n=5) |> knitr::kable(digits = 2)| artist_name | track_name | track_number | album_name | album_release_year | album_release_date | valence | energy | danceability | loudness | tempo | speechiness | instrumentalness | liveness | key_name | mode_name | key_mode | duration_ms |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Tame Impala | Apocalypse Dreams | 3 | Lonerism | 2012 | 2012-01-01 | 0.27 | 0.93 | 0.50 | -2.28 | 128.00 | 0.05 | 0.13 | 0.07 | G# | major | G# major | 356946 |
| Tame Impala | Borderline | 3 | The Slow Rush | 2020 | 2020-02-14 | 0.87 | 0.87 | 0.62 | -3.07 | 97.96 | 0.04 | 0.00 | 0.08 | F | minor | F minor | 237800 |
| Tame Impala | Be Above It | 1 | Lonerism | 2012 | 2012-01-01 | 0.38 | 0.98 | 0.75 | -6.07 | 118.97 | 0.07 | 0.27 | 0.11 | E | minor | E minor | 201960 |
| Tame Impala | Mind Mischief | 4 | Lonerism | 2012 | 2012-01-01 | 0.82 | 0.94 | 0.25 | -3.23 | 161.33 | 0.05 | 0.00 | 0.37 | F# | major | F# major | 271880 |
| Tame Impala | Expectation | 8 | InnerSpeaker | 2010 | 2010-05-21 | 0.18 | 0.84 | 0.45 | -5.14 | 139.94 | 0.06 | 0.00 | 0.07 | F | major | F major | 362466 |
Data Preparation
Since there are some duplicated tracks, I’ve cleaned them. Also, I removed the album with the live versions.
# Remove records with the same track names
df <- df |> distinct(track_name, .keep_all = TRUE)
# Filter out albums that include "Live Versions"
df <- df |> filter(!grepl("Live Versions", album_name))
# Change the data type of album_name from numeric into character
df$album_name <- as.character(df$album_name)Album analysis (valence and energy)
# Change album_name from character to factor before plotting
df$album_name <- as.factor(df$album_name)
# Plot a boxplot
ggplot(df, aes(x=album_name, y=valence))+
geom_boxplot()+
labs(x="Album name", y="Valence")Interestingggg, seems like Tame Impala has been the happiest in his last album from 2020! How about his energy?
Energy
ggplot(df, aes(x=album_name, y=energy))+
geom_boxplot()+
labs(x="Album name", y="Energy")Looks like “Lonerism” is the most energetic album!
Fun fact: The songs for “Lonerism” were made world-wide: he has recorded guitar in Vienna and vocals on a Singapore-London flight. He has also lost an iPod somewhere between London and Amsterdam (full with recorded ideas), but he finally finished the album in a rented apartment in Paris in 2011. Such an international album, wow!
I wonder which song from this album is the most energetic one:
library(dplyr)
df %>%
slice_max(energy, n = 1) %>%
select(track_name, album_name, album_release_year, energy) %>%
knitr::kable(digits = 2)| track_name | album_name | album_release_year | energy |
|---|---|---|---|
| Be Above It | Lonerism | 2012 | 0.98 |
“Be Above It” it is! That´s a good one.
Danceability
Personally, I love dancing in my room to my favorite albums- how about you? Let´s check which album would be the best choice in this case:
ggplot(df, aes(x=album_name, y=danceability))+
geom_boxplot()+
labs(x="Album name", y="Danceability")Seems like it´s “The Slow Rush”!
Speaking of “Rush”, let´s rush into finding Tame Impala´s most positive songs!!!
Most positive songs
df |> slice_max(valence, n=3) |> dplyr::select(track_name, album_name, album_release_year, valence) |> knitr::kable(digits = 2)| track_name | album_name | album_release_year | valence |
|---|---|---|---|
| Borderline | The Slow Rush | 2020 | 0.87 |
| Mind Mischief | Lonerism | 2012 | 0.82 |
| Disciples | Currents | 2015 | 0.80 |
Fun fact: Borderline is one of my favorite songs! Let´s give it a listen for some positive feelings!
General Mood
Let´s take a look at the valence and energy levels together to get an overview of the albums´ general mood:
library(ggplot2)
library(ggplot2)
ggplot(df, aes(x = valence, y = energy, color = album_name)) +
geom_point() +
geom_hline(yintercept = 0.5, color = "grey") +
geom_vline(xintercept = 0.5, color = "grey")Impressive! The albums appear to be energetically charged! In terms of valence, “Lonerism,” true to its name, leans towards a more negative and melancholic tone, whereas the other albums encompass a spectrum of emotions, from sadness to joy!
Conclusion
So, in the cosmic dance of Tame Impala’s SpotifyR report, it’s not just data; it’s a funky odyssey through beats, rhythms, and vibes. From head-nodding beats to soul-searching melodies, Tame Impala’s musical universe is a groove-packed journey.
Thank you for reading!